1. Field of the Invention
This invention relates to the automatic classification of sequences of symbols, in particular of sequences of words for use in the production of a dialogue model, in particular to the production of a dialogue model for natural language automated call routing systems. This invention also relates to the generation of an insignificant symbol set and of an equivalent symbol sequence pair set for use in such automatic classification.
2. Detailed Description of Related Art
In a call routing service utilising a human operator, user requests may be categorised into 4 types. An explicit user request is where the user knows the service which is required, for example “Could you put me through to directory enquiries please?”. An implicit user request is where the user does not explicitly name the service required, for example “Can I have the number for . . . please?”. A general problem description is where the customer does not know which service they require, but expects the operator to be able to help. The operator generally engages in a dialogue in order to identify the required service. The final category is ‘other’ where there is confusion about the problem, or what the service can do.
Automated call routing can be achieved by the use of a touch tone menu in an interactive voice response (IVR) system. It is widely accepted that these systems can be difficult to use, and much skill is needed in the design of suitable voice menu prompts. Even designs using best-practice have several fundamental weaknesses. In particular, the mapping from system function to user action (pressing a key) is usually completely arbitrary and therefore difficult to remember. To alleviate this problem, menus must be kept very short, which can lead to complex hierarchical menu structures which are difficult to navigate. In addition, many users have significant difficulty in mapping their requirements onto one of the listed system options. Touch tone IVR systems can be effective for explicit user requests, may sometimes work with implicit user requests, but are inappropriate for general problem descriptions or confused users.
Spoken menu systems are the natural extension of touch tone IVR systems which use speech recognition technology. Their main advantages are a reduction in the prompt length, and a direct relationship between meaning and action—for example saying the word ‘operator’ rather than pressing an arbitrary key. However, many of the limitations of touch tone systems remain: the difficulty of mapping customer requirements onto the menu options, and a strictly hierarchical navigation structure. There is also the added difficulty of non-perfect speech recognition performance, and the consequent need for error recovery strategies.
Word spotting can be used in a system which accepts a natural language utterance from a user. For some applications word spotting is a useful approach to task identification. However some tasks, for example line test requests are characterised by high frequencies of problem specification, so it is difficult if not impossible to determine the task which is required using word spotting techniques.
The use of advanced topic identification techniques to categorise general problem descriptions in an automated natural language call steering system is the subject of ongoing research, for example, the automated service described by A. L. Gorin et al in “How May I Help You” Proc of IVTTA, pp57-60, Basking Ridge, September 1996, uses automatically acquired salient phrase fragments for call classification. In contrast, other studies either do not consider this type of request at all, or attempt to exclude them from automatic identification.
In the above reference automated service, a classifier is trained using a set of speech utterances which are categorised as being directed to ones of a set of predetermined set of tasks. The problem which this prior art system is that the tasks need to be predetermined, and in this case are defined to be the operator action resulting from the entire interaction. The relationship between the required action, and the operator dialogue necessary to determine the action is not easily discovered. In a manual call routing system there are often multiple dialogue turns before an operator action occurs. It is desirable for an automated natural language call steering system to behave in a similar way to a manually operated call steering system for at least a subset of operator supplied services. In order to do this it is necessary to have a dialogue model which can deal with a range of different styles of enquiries.